{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ad29a6dd",
   "metadata": {},
   "source": [
    "## The Impact of COVID-19 on Technical Services Units\n",
    "\n",
    "These survey results are compiled from a survey conducted from October 18, 2021 to January 1, 2022 regarding the measurable impacts of COVID-19 on technical services units within the United States. The survey was widely distributed via Oklahoma library listservs, as well as through discussion groups in ALA Connect and technical services or cataloging-specific listservs or groups. \n",
    "\n",
    "Data was downloaded from Qualtrics (the survey gathering tool) into an Exel file. Due to a techncial malfunction, IP addresses were collected by Qualtrics, despite setting up the survey to exclude this data. As such, the data is treated as sensitive information. This initial file was saved as the raw data. The raw version of this file containing possibly identifiable information is stored in an encrypted file. Note that the encryption key for this file is stored offline. The duplicated and anonymized version of this data is utilized as the \"raw data file\" and is available in a raw, CSV format within the Harvard Dataverse here: https://doi.org/10.7910/DVN/ASTFMH. \n",
    "\n",
    "The origin clean version of data removed fields auto-populated by Qualtrics, including possibly identifiable infomation columns (StartDate, EndDate, Status, IPAddress, Progress, Duration (in seconds), Finished, RecordedDate, ResponseId, RecipientLastName, RecipientFirstName, RecipientEmail, ExternalReference, LocationLatitude, LocationLongitude, DistributionChannel, UserLanguage). The cleaned version of this data is available within the Harvard Dataverse here: https://doi.org/10.7910/DVN/BMHGPY. \n",
    "\n",
    "Data was then standardized in a last pass to remove completely-blank rows were removed within Excel. The raw and origin clean files contain 832 total rows of data; 72 completely-blank rows were removed, resulting in 760 remaining data rows. Two additional rows of data containing the survey questions and ImportId were also removed to streamline the data analysis process, resulting in a final tally of 758 rows of data. Qualtric's unordered question ennumeration (Q10, Q40, Q33, et cetera) was replaced with a new numberical order (Q1, Q2, Q3, et cetera) to help with data analysis shorthand in Python. \n",
    "\n",
    "Three versions of the clean file were saved into CSV formats: Impact_of_COVID_on_Tech_Services_Clean_1.CSV, the version of the file containing questions and Q-numbers, Impact_of_COVID_on_Tech_Services_Clean_2.CSV, the version of the file lacking the actual survey questions, thus relying on Q-numbers to correlate to the first of the two clean files, and Impact_of_COVID_on_Tech_Services_Clean_3.CSV, which removed all responses that did not answer past Q4 (the first section of the survey). Four Q36 (What state is your library located in?) rows were also removed for being outside the scope of the study, as respondees were located in Australia or Canada. Impact_of_COVID_on_Tech_Services_Clean_3.CSV also standardizes all state names. \n",
    "\n",
    "All three files were saved separately to demonstrate the major file changes that took place during data standardization with Impact_of_COVID_on_Tech_Services_Clean_3.CSV ultimately used for the bulk of subsequent data analyses. All three files are availalbe within the Harvard Dataverse here: https://doi.org/10.7910/DVN/DGBUV7.  \n",
    "\n",
    "This Python script seeks to: fill in NaN responses where data is missing, to analyze the data holistically for trends (many libraries had a measureable COVID-19 impact on their technical services units/procedures or did not, et cetera). \n",
    "\n",
    "The analyzed dataset was later clustered by library type to identify type-trends, staffing numbers pre- and post-COVID to standardize numbering outside of the cleaned datasets, and by qualitative responses for Q12, Q20, and Q34 so reoccurring words could be counted.\n",
    "\n",
    "Links to related Python script for this study:\n",
    "COVID_Impact_TechnicalServices_HolisticAnalysis (a holistic analysis of all survey data): https://doi.org/10.7910/DVN/SXMSDZ\n",
    "COVID_Impact_TechnicalServices_LibraryTypeAnalysis (a clustered analysis of impact by library type): https://doi.org/10.7910/DVN/SXMSDZ\n",
    "\n",
    "The researcher specifically aims to invesigate whether COVID-19 had an immediate or lasting impact on library technical services units or procedures and aims to keep this investigation in mind when analyzing data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90b70629",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setup Numpy and Pandas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Import Matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "42896d90",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Q1              Q2                    Q3   Q4  \\\n",
      "0          Yes              No            4-6 Months   No   \n",
      "1          Yes       As-Needed        1 Year or more  Yes   \n",
      "2           No              No        Not applicable   No   \n",
      "3          Yes             Yes        1 Year or more  Yes   \n",
      "4          Yes              No  Three Months or less   No   \n",
      "..         ...             ...                   ...  ...   \n",
      "657        NaN  Not applicable        Not applicable  NaN   \n",
      "658        Yes             Yes        1 Year or more  NaN   \n",
      "659  As-Needed              No  Three Months or less   No   \n",
      "660        Yes       As-Needed            4-6 Months  Yes   \n",
      "661        NaN  Not applicable        Not applicable  NaN   \n",
      "\n",
      "                                                    Q5   Q6   Q7  \\\n",
      "0                   Cataloging,Processing,Acquisitions    8    9   \n",
      "1                                           Cataloging  NaN  NaN   \n",
      "2                   Cataloging,Processing,Acquisitions   11   11   \n",
      "3    Cataloging,Processing,Electronic Resources,Acq...    2    2   \n",
      "4    Cataloging,Processing,Acquisitions,Collection ...    3    3   \n",
      "..                                                 ...  ...  ...   \n",
      "657  Cataloging,Processing,Interlibrary Loan,Acquis...    2    2   \n",
      "658  Cataloging,Electronic Resources,Acquisitions,L...  2.5  2.5   \n",
      "659  Cataloging,Processing,Electronic Resources,Acq...    2    2   \n",
      "660         Cataloging,Processing,Electronic Resources    3    3   \n",
      "661                                              Other  NaN  NaN   \n",
      "\n",
      "                                                    Q8   Q9             Q10  \\\n",
      "0    We did not temporarily or permanently lose sta...  NaN              No   \n",
      "1                                 Voluntary Retirement   No              No   \n",
      "2    We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "3    We did not temporarily or permanently lose sta...  NaN              No   \n",
      "4    We did not temporarily or permanently lose sta...  NaN              No   \n",
      "..                                                 ...  ...             ...   \n",
      "657  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "658  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "659  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "660  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "661                                     Not applicable  NaN  Not applicable   \n",
      "\n",
      "     ...                                                Q29  Q30  \\\n",
      "0    ...                               Change to a workflow  NaN   \n",
      "1    ...   Change to work environment (e.g. work-from-home)  NaN   \n",
      "2    ...                                                NaN  NaN   \n",
      "3    ...  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "4    ...                                                NaN  NaN   \n",
      "..   ...                                                ...  ...   \n",
      "657  ...                                                NaN  NaN   \n",
      "658  ...  Change to a workflow,Change to documentation,C...  NaN   \n",
      "659  ...                                                NaN  NaN   \n",
      "660  ...             Change to Cataloging and/or Processing  NaN   \n",
      "661  ...                                                NaN  NaN   \n",
      "\n",
      "                                                   Q31  \\\n",
      "0    I strongly agree that these changes have been ...   \n",
      "1    I strongly agree that these changes have been ...   \n",
      "2                                       Not applicable   \n",
      "3    I strongly agree that these changes have been ...   \n",
      "4                                       Not applicable   \n",
      "..                                                 ...   \n",
      "657                                                NaN   \n",
      "658  I strongly agree that these changes have been ...   \n",
      "659                                     Not applicable   \n",
      "660  I somewhat agree that these changes have been ...   \n",
      "661                                                NaN   \n",
      "\n",
      "                                                   Q32                 Q33  \\\n",
      "0    I somewhat agree that these changes will have ...   Somewhat positive   \n",
      "1    I neither agree nor disagree on the impact tha...  Extremely positive   \n",
      "2                                       Not applicable      Not applicable   \n",
      "3    I strongly agree that these changes will have ...  Extremely positive   \n",
      "4                                       Not applicable      Not applicable   \n",
      "..                                                 ...                 ...   \n",
      "657                                                NaN                 NaN   \n",
      "658  I strongly agree that these changes will have ...  Extremely positive   \n",
      "659                                     Not applicable      Not applicable   \n",
      "660  I somewhat agree that these changes will have ...   Somewhat positive   \n",
      "661                                                NaN                 NaN   \n",
      "\n",
      "      Q34  Q35         Q36                                 Q37  \\\n",
      "0      no   No    Illinois                      Public Library   \n",
      "1     NaN   No         NaN                      Public Library   \n",
      "2     NaN   No  Washington                      Public Library   \n",
      "3     NaN   No        Ohio  Special Collections and/or Archive   \n",
      "4     NaN   No        Ohio                      Public Library   \n",
      "..    ...  ...         ...                                 ...   \n",
      "657   NaN  NaN         NaN                                 NaN   \n",
      "658  Nope   No      Oregon                      Public Library   \n",
      "659   NaN  Yes    Oklahoma                 K-12 School Library   \n",
      "660   NaN   No    Illinois                    Academic Library   \n",
      "661   NaN  NaN         NaN                                 NaN   \n",
      "\n",
      "                                Q38  \n",
      "0    Manager, Supervisor, Unit Head  \n",
      "1                         Librarian  \n",
      "2                         Librarian  \n",
      "3    Manager, Supervisor, Unit Head  \n",
      "4                         Librarian  \n",
      "..                              ...  \n",
      "657                             NaN  \n",
      "658                       Librarian  \n",
      "659                       Librarian  \n",
      "660  Manager, Supervisor, Unit Head  \n",
      "661                             NaN  \n",
      "\n",
      "[662 rows x 38 columns]\n"
     ]
    }
   ],
   "source": [
    "#Import File\n",
    "cov=pd.read_csv('Impact_of_COVID_on_Tech_Services_Clean_3.csv')\n",
    "\n",
    "print(cov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0d1694ef",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Q1         Q2                    Q3   Q4  \\\n",
      "0  Yes         No            4-6 Months   No   \n",
      "1  Yes  As-Needed        1 Year or more  Yes   \n",
      "2   No         No        Not applicable   No   \n",
      "3  Yes        Yes        1 Year or more  Yes   \n",
      "4  Yes         No  Three Months or less   No   \n",
      "\n",
      "                                                  Q5   Q6   Q7  \\\n",
      "0                 Cataloging,Processing,Acquisitions    8    9   \n",
      "1                                         Cataloging  NaN  NaN   \n",
      "2                 Cataloging,Processing,Acquisitions   11   11   \n",
      "3  Cataloging,Processing,Electronic Resources,Acq...    2    2   \n",
      "4  Cataloging,Processing,Acquisitions,Collection ...    3    3   \n",
      "\n",
      "                                                  Q8   Q9             Q10  \\\n",
      "0  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "1                               Voluntary Retirement   No              No   \n",
      "2  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "3  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "4  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "\n",
      "   ...                                                Q29  Q30  \\\n",
      "0  ...                               Change to a workflow  NaN   \n",
      "1  ...   Change to work environment (e.g. work-from-home)  NaN   \n",
      "2  ...                                                NaN  NaN   \n",
      "3  ...  Change to Cataloging and/or Processing,Change ...  NaN   \n",
      "4  ...                                                NaN  NaN   \n",
      "\n",
      "                                                 Q31  \\\n",
      "0  I strongly agree that these changes have been ...   \n",
      "1  I strongly agree that these changes have been ...   \n",
      "2                                     Not applicable   \n",
      "3  I strongly agree that these changes have been ...   \n",
      "4                                     Not applicable   \n",
      "\n",
      "                                                 Q32                 Q33  Q34  \\\n",
      "0  I somewhat agree that these changes will have ...   Somewhat positive   no   \n",
      "1  I neither agree nor disagree on the impact tha...  Extremely positive  NaN   \n",
      "2                                     Not applicable      Not applicable  NaN   \n",
      "3  I strongly agree that these changes will have ...  Extremely positive  NaN   \n",
      "4                                     Not applicable      Not applicable  NaN   \n",
      "\n",
      "  Q35         Q36                                 Q37  \\\n",
      "0  No    Illinois                      Public Library   \n",
      "1  No         NaN                      Public Library   \n",
      "2  No  Washington                      Public Library   \n",
      "3  No        Ohio  Special Collections and/or Archive   \n",
      "4  No        Ohio                      Public Library   \n",
      "\n",
      "                              Q38  \n",
      "0  Manager, Supervisor, Unit Head  \n",
      "1                       Librarian  \n",
      "2                       Librarian  \n",
      "3  Manager, Supervisor, Unit Head  \n",
      "4                       Librarian  \n",
      "\n",
      "[5 rows x 38 columns]\n",
      "            Q1              Q2                    Q3   Q4  \\\n",
      "657        NaN  Not applicable        Not applicable  NaN   \n",
      "658        Yes             Yes        1 Year or more  NaN   \n",
      "659  As-Needed              No  Three Months or less   No   \n",
      "660        Yes       As-Needed            4-6 Months  Yes   \n",
      "661        NaN  Not applicable        Not applicable  NaN   \n",
      "\n",
      "                                                    Q5   Q6   Q7  \\\n",
      "657  Cataloging,Processing,Interlibrary Loan,Acquis...    2    2   \n",
      "658  Cataloging,Electronic Resources,Acquisitions,L...  2.5  2.5   \n",
      "659  Cataloging,Processing,Electronic Resources,Acq...    2    2   \n",
      "660         Cataloging,Processing,Electronic Resources    3    3   \n",
      "661                                              Other  NaN  NaN   \n",
      "\n",
      "                                                    Q8   Q9             Q10  \\\n",
      "657  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "658  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "659  We did not temporarily or permanently lose sta...  NaN              No   \n",
      "660  We did not temporarily or permanently lose sta...  NaN  Not applicable   \n",
      "661                                     Not applicable  NaN  Not applicable   \n",
      "\n",
      "     ...                                                Q29  Q30  \\\n",
      "657  ...                                                NaN  NaN   \n",
      "658  ...  Change to a workflow,Change to documentation,C...  NaN   \n",
      "659  ...                                                NaN  NaN   \n",
      "660  ...             Change to Cataloging and/or Processing  NaN   \n",
      "661  ...                                                NaN  NaN   \n",
      "\n",
      "                                                   Q31  \\\n",
      "657                                                NaN   \n",
      "658  I strongly agree that these changes have been ...   \n",
      "659                                     Not applicable   \n",
      "660  I somewhat agree that these changes have been ...   \n",
      "661                                                NaN   \n",
      "\n",
      "                                                   Q32                 Q33  \\\n",
      "657                                                NaN                 NaN   \n",
      "658  I strongly agree that these changes will have ...  Extremely positive   \n",
      "659                                     Not applicable      Not applicable   \n",
      "660  I somewhat agree that these changes will have ...   Somewhat positive   \n",
      "661                                                NaN                 NaN   \n",
      "\n",
      "      Q34  Q35       Q36                  Q37                             Q38  \n",
      "657   NaN  NaN       NaN                  NaN                             NaN  \n",
      "658  Nope   No    Oregon       Public Library                       Librarian  \n",
      "659   NaN  Yes  Oklahoma  K-12 School Library                       Librarian  \n",
      "660   NaN   No  Illinois     Academic Library  Manager, Supervisor, Unit Head  \n",
      "661   NaN  NaN       NaN                  NaN                             NaN  \n",
      "\n",
      "[5 rows x 38 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Analysis\n",
    "print(cov.head()) #Column names, data samples\n",
    "print(cov.tail()) #Tail sample, 662 rows\n",
    "cov.describe() #5 rows with 38 columns\n",
    "type(cov) #Confirming is dataframe type\n",
    "\n",
    "#cov.dtypes is Object for all 38 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d246aa64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes          543\n",
      "No            73\n",
      "As-Needed     38\n",
      "Name: Q1, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 1 Value Counts\n",
    "#Did your technical services unit or any members within your unit work remotely during the COVID-19 pandemic?\n",
    "Q1=cov['Q1'].value_counts()\n",
    "print(Q1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "910c7752",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No                375\n",
      "Yes               144\n",
      "As-Needed         125\n",
      "Not applicable     18\n",
      "Name: Q2, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 2 Value Counts\n",
    "#Is your technical services unit or any members within your unit still working remotely because of the COVID-19 pandemic?\n",
    "Q2=cov['Q2'].value_counts()\n",
    "print(Q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2236aa1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Year or more          246\n",
      "Three Months or less    180\n",
      "4-6 Months               97\n",
      "Not applicable           81\n",
      "7-9 Months               39\n",
      "10-11 Months             17\n",
      "Name: Q3, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 3 Value Counts\n",
    "#If your department worked remotely during the COVID-19 pandemic, how long did they work remotely for?\n",
    "Q3=cov['Q3'].value_counts()\n",
    "print(Q3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "858492e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No              422\n",
      "Yes             187\n",
      "I'm Not Sure     26\n",
      "Name: Q4, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 4 Value Counts\n",
    "#Has your technical services unit or any members within your unit migrated to part-time or full-time remote work post-COVID-19?\n",
    "Q4=cov['Q4'].value_counts()\n",
    "print(Q4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6edefb29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cataloging                                         616\n",
       "Processing                                         550\n",
       "Acquisitions                                       459\n",
       "Electronic Resources                               383\n",
       "Collection Development                             301\n",
       "Library Systems (e.g. Discovery or ILS support)    258\n",
       "Interlibrary Loan                                  202\n",
       "Archives and/or Special Collections                160\n",
       "Federal Depository Library Program                  76\n",
       "Other                                               66\n",
       "nan                                                  3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Question 5 Value Counts\n",
    "#Which areas is your technical services team comprised of? If you do not work in a technical services team or unit (e.g. a solo librarian), which technical tasks do you oversee?\n",
    "#Please select all that apply.\n",
    "\n",
    "#Convert column type to string\n",
    "cov['Q5'] = cov['Q5'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "cov['Q5'].str.split(',', expand=True).stack().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1b35e972",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Q6    Q7\n",
      "0     8.0   9.0\n",
      "1    11.0  11.0\n",
      "2     2.0   2.0\n",
      "3     3.0   3.0\n",
      "4    10.0  10.0\n",
      "..    ...   ...\n",
      "627   2.0   2.0\n",
      "628   2.0   2.0\n",
      "629   2.5   2.5\n",
      "630   2.0   2.0\n",
      "631   3.0   3.0\n",
      "\n",
      "[632 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "#Question 6 and 7 Counts\n",
    "#Data in Questions 6 and 7 pertaining to staffing levels pre- and post-COVID were extracted into a new CSV file (titled Impact_of_COVID_on_Tech_Services_Staffing_Clean.csv), to standardize numbering for mathematical data analysis. \n",
    "staffing=pd.read_csv('Impact_of_COVID_on_Tech_Services_Staffing_Clean_3.csv')\n",
    "print(staffing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cd835246",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       8.0\n",
      "1      11.0\n",
      "2       2.0\n",
      "3       3.0\n",
      "4      10.0\n",
      "       ... \n",
      "627     2.0\n",
      "628     2.0\n",
      "629     2.5\n",
      "630     2.0\n",
      "631     3.0\n",
      "Name: Q6, Length: 632, dtype: float64\n",
      "6.392563291139241\n",
      "4040.1\n"
     ]
    }
   ],
   "source": [
    "#Question 6\n",
    "#\"How many full and part-time staff members (not student workers) were on your Technical Services team prior to the COVID-19 pandemic?\n",
    "#If you are a solo librarian, the only individual who oversees technical tasks in your library, et cetera, please indicate \"\"1\"\".\"\n",
    "Q6=staffing['Q6']\n",
    "print(Q6)\n",
    "\n",
    "#Average\n",
    "staffing2=staffing['Q6'].mean()\n",
    "print(staffing2)\n",
    "\n",
    "#Sum\n",
    "staffing3=staffing['Q6'].sum()\n",
    "print(staffing3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "67639f65",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       9.0\n",
      "1      11.0\n",
      "2       2.0\n",
      "3       3.0\n",
      "4      10.0\n",
      "       ... \n",
      "627     2.0\n",
      "628     2.0\n",
      "629     2.5\n",
      "630     2.0\n",
      "631     3.0\n",
      "Name: Q7, Length: 632, dtype: float64\n",
      "5.813686708860759\n",
      "3674.25\n"
     ]
    }
   ],
   "source": [
    "#Question 7\n",
    "#How many full and part-time staff members (not student workers) are on your technical services team now?\n",
    "#If you are a solo librarian, the only individual who oversees technical tasks in your library, et cetera, please indicate \"1\".\n",
    "Q7=staffing['Q7']\n",
    "print(Q7)\n",
    "\n",
    "#Average\n",
    "staffing4=staffing['Q7'].mean()\n",
    "print(staffing4)\n",
    "\n",
    "#Sum\n",
    "staffing5=staffing['Q7'].sum()\n",
    "print(staffing5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ab4fb3ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "We did not temporarily or permanently lose staff because of the COVID-19 pandemic    379\n",
       "Voluntary Retirement                                                                 117\n",
       "Furloughs                                                                             76\n",
       "Resignation                                                                           73\n",
       "Other                                                                                 44\n",
       "Not applicable                                                                        39\n",
       "Lay-Offs                                                                              38\n",
       "nan                                                                                    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Question 8 Value Counts\n",
    "#\"Did your technical services unit experience furloughs, lay-offs, voluntary retirements, or similar as a direct result of the COVID-19 pandemic?\n",
    "#Please select all that apply.\"\n",
    "\n",
    "#Convert column type to string\n",
    "cov['Q8'] = cov['Q8'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "cov['Q8'].str.split(',', expand=True).stack().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "87300a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No              141\n",
      "Yes             118\n",
      "I'm not sure     14\n",
      "Name: Q9, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 9 Value Counts\n",
    "#If applicable, has your technical services unit recovered any positions lost to furloughs, lay-offs, voluntary retirement, or similar specifically related to COVID-19? This may include refilling positions vacated because of the COVID-19 pandemic.\n",
    "\n",
    "Q9=cov['Q9'].value_counts()\n",
    "print(Q9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bda14b82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No                350\n",
      "Not applicable    165\n",
      "Yes               104\n",
      "I'm not sure       40\n",
      "Name: Q10, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 10 Value Counts\n",
    "#Did your technical services unit lose a significant amount of institutional knowledge or skill as a direct result of COVID-19 staffing changes?\n",
    "\n",
    "Q10=cov['Q10'].value_counts()\n",
    "print(Q10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d23331be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes             356\n",
      "No              249\n",
      "I'm not sure     37\n",
      "Name: Q11, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 11 Value Counts\n",
    "#Has your technical service unit or library struggled with workload, backlog, maintaining quality of service, or similar issues as a direct result of the impacts of the COVID-19 pandemic?\n",
    "\n",
    "Q11=cov['Q11'].value_counts()\n",
    "print(Q11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e97b4274",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Question 12 Value Counts\n",
    "#If applicable, what has been the impact of furloughs, lay-offs, or voluntary retirement on your technical services unit as a result of the COVID-19 pandemic?\n",
    "\n",
    "#Open-Ended Answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "192df2f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No              366\n",
      "Yes             171\n",
      "I'm not sure     97\n",
      "Name: Q13, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 13 Value Counts\n",
    "#Did your library lose funding or a portion of funding that impacted work for your technical services unit?\n",
    "\n",
    "Q13=cov['Q13'].value_counts()\n",
    "print(Q13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "789ad4cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan                                            402\n",
       "Funds to purchase new items                    108\n",
       "Funding for positions                           93\n",
       " including for hiring new positions             93\n",
       " hiring student workers                         93\n",
       " or maintaining previous staffing numbers       93\n",
       "I'm not sure                                    91\n",
       "Professional development or traveling funds     75\n",
       "Electronic resources and/or eBooks funds        55\n",
       "Other                                           33\n",
       "Processing materials                            27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Question 14 Value Counts\n",
    "#\"If your library lost funding or a portion of funding that impacted work for your technical services unit, what did that funding go towards? \n",
    "#Please select all that apply.\"\n",
    "\n",
    "#Convert column type to string\n",
    "cov['Q14'] = cov['Q14'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "cov['Q14'].str.split(',', expand=True).stack().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "087f32ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Question 15 Value Counts\n",
    "#If you selected \"Other\", please explain:\n",
    "\n",
    "#Open-Ended Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f0db4529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reduced access to materials because of shipping issues    352\n",
       " ordering issues                                          352\n",
       " or funding issues                                        352\n",
       "Reduced speeds in turnaround time or regular work         290\n",
       "Hiring freezes                                            189\n",
       "Restrictions on raises or salary increases                167\n",
       "nan                                                       159\n",
       "Restrictions on promotions                                 44\n",
       "Other                                                      38\n",
       "I'm not sure                                               17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Question 16 Value Counts\n",
    "#\"Did your technical services unit encounter other disruptions to normal staffing or salaries because of the COVID-19 pandemic? \n",
    "#Please select all that apply.\"\n",
    "\n",
    "#Convert column type to string\n",
    "cov['Q16'] = cov['Q16'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "cov['Q16'].str.split(',', expand=True).stack().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "af37fad8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No              546\n",
      "Yes              64\n",
      "I'm not sure     17\n",
      "Name: Q17, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 17 Value Counts\n",
    "#Did your library or technical services unit outsource portions or all of its technical tasks to a vendor during the COVID-19 pandemic?\n",
    "\n",
    "Q17=cov['Q17'].value_counts()\n",
    "print(Q17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cae3c97f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No              121\n",
      "Yes               8\n",
      "I'm not sure      8\n",
      "Name: Q18, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 18 Value Counts\n",
    "#If your library or technical services unit outsourced portions or all of its technical tasks to a vendor during the COVID-19 pandemic, was it as a direct result of the COVID-19 pandemic?\n",
    "\n",
    "Q18=cov['Q18'].value_counts()\n",
    "print(Q18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "73443a46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes             597\n",
      "No               41\n",
      "I'm not sure      6\n",
      "Name: Q19, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 19 Value Counts\n",
    "#Did your library restrict or revise their services during the COVID-19 pandemic?\n",
    "\n",
    "Q19=cov['Q19'].value_counts()\n",
    "print(Q19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5c9411e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Question 20 Value Counts\n",
    "#How did these restrictions or revisions impact technical services work?\n",
    "\n",
    "#Open-Ended Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "275a8a94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes             495\n",
      "No              135\n",
      "I'm not sure     15\n",
      "Name: Q21, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 21 Value Counts\n",
    "#Did members of your technical services unit work on special remote projects or clean-up projects during the COVID-19 pandemic?\n",
    "\n",
    "Q21=cov['Q21'].value_counts()\n",
    "print(Q21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e6747c20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Record-Specific Clean-Up (e.g. standardizing cataloging records or local authority files)    379\n",
       "Cataloging and/or Processing of materials                                                    304\n",
       "Training                                                                                     286\n",
       "Documentation/Revision of Documentation                                                      249\n",
       "Data-Specific Clean-Up (e.g. a data reclamation project)                                     177\n",
       "Working on Backlog                                                                           146\n",
       "nan                                                                                          134\n",
       "Other                                                                                        101\n",
       "Creation Projects (e.g. Creating LibGuides or Digital Exhibits)                               83\n",
       "Retrospective Processing Projects                                                             80\n",
       "Migration (Between Platforms                                                                  67\n",
       " ILSes                                                                                        67\n",
       " etc)                                                                                         67\n",
       "Conversion Projects (e.g. between cataloging standards)                                       20\n",
       "I'm not sure                                                                                  10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Question 22 Value Counts\n",
    "#\"If so, what types of projects did your technical services unit work on during the COVID-19 pandemic? \n",
    "#Please select all that apply.\"\n",
    "\n",
    "#Convert column type to string\n",
    "cov['Q22'] = cov['Q22'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "cov['Q22'].str.split(',', expand=True).stack().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "52762c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Question 23 Value Counts\n",
    "#If you selected \"Other\", please explain:\n",
    "\n",
    "#Open-Ended Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6ee6283b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes                             426\n",
      "No                              121\n",
      "Other (Please explain below)     50\n",
      "I'm not sure                     13\n",
      "Name: Q24, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 24 Value Counts\n",
    "#Has your technical services unit returned to their pre-COVID-19 (\"normal\") services yet?\n",
    "\n",
    "Q24=cov['Q24'].value_counts()\n",
    "print(Q24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3584340b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Question 25 Value Counts\n",
    "#If you selected \"Other\", please explain:\n",
    "\n",
    "#Open-Ended Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "75b3584c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes                             296\n",
      "No                              172\n",
      "I'm not sure                     70\n",
      "Other (Please explain below)     18\n",
      "Name: Q26, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 26 Value Counts\n",
    "#Has your technical services unit decided to keep a project, process, or workflow implemented during the COVID-19 pandemic as a permanent addition to prior services?\n",
    "\n",
    "Q26=cov['Q26'].value_counts()\n",
    "print(Q26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1decd38b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Question 27 Value Counts\n",
    "#If you selected \"Other\", please explain:\n",
    "\n",
    "#Open-Ended Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a5629347",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No              164\n",
      "I'm not sure    157\n",
      "Yes             104\n",
      "Name: Q28, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 28 Value Counts\n",
    "#\"Would your technical services unit have adopted these changes regardless of the COVID-19 pandemic? \n",
    "#For example, planning a migration that would have proceeded regardless of working from home.\"\n",
    "\n",
    "Q28=cov['Q28'].value_counts()\n",
    "print(Q28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "014f7118",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan                                                 314\n",
       "Change to a workflow                                204\n",
       "Change to work environment (e.g. work-from-home)    157\n",
       "Change in position descriptions or work duties      119\n",
       "Change to Cataloging and/or Processing              116\n",
       "Change to Electronic Resources and/or eBooks         98\n",
       "Change to Acquisitions                               98\n",
       " Collection Development                              98\n",
       " or Book Ordering                                    98\n",
       "Change to documentation                              95\n",
       "Change to training                                   60\n",
       "Change in organizational structure                   48\n",
       "Other                                                31\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Question 29 Value Counts\n",
    "#\"If your technical services unit decided to keep a project, process, or workflow implemented during the COVID-19 pandemic as a permanent addition to prior services, what type of change has been adopted? \n",
    "#Please select all that apply.\"\n",
    "\n",
    "#Convert column type to string\n",
    "cov['Q29'] = cov['Q29'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "cov['Q29'].str.split(',', expand=True).stack().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ab0129cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Question 30 Value Counts\n",
    "#If you selected \"Other\", please explain:\n",
    "\n",
    "#Open-Ended Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3b881b16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not applicable                                                     247\n",
      "I somewhat agree that these changes have been successful           163\n",
      "I strongly agree that these changes have been successful           140\n",
      "I neither agree nor disagree about the success of these changes     41\n",
      "I somewhat disagree about the success of these changes              15\n",
      "I strongly disagree about the success of these changes               6\n",
      "Name: Q31, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 31 Value Counts\n",
    "#In your personal opinion, have these changes been successful?\n",
    "\n",
    "Q31=cov['Q31'].value_counts()\n",
    "print(Q31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a1ea57fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not applicable                                                                        247\n",
      "I strongly agree that these changes will have a lasting impact on my unit             174\n",
      "I somewhat agree that these changes will have a lasting impact on my unit             137\n",
      "I neither agree nor disagree on the impact that these changes will have on my unit     44\n",
      "I somewhat disagree that these changes will have a lasting impact on my unit            7\n",
      "I disagree that these changes will have a lasting impact on my unit                     2\n",
      "Name: Q32, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 32 Value Counts\n",
    "#In your personal opinion, will these changes have a lasting impact on your technical services unit and the services provided by that unit?\n",
    "\n",
    "Q32=cov['Q32'].value_counts()\n",
    "print(Q32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "460c44bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not applicable                   244\n",
      "Somewhat positive                162\n",
      "Extremely positive               127\n",
      "Neither positive nor negative     39\n",
      "Somewhat negative                 32\n",
      "Extremely negative                 5\n",
      "Name: Q33, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 33 Value Counts\n",
    "#In your personal opinion, how do you view these changes?\n",
    "\n",
    "Q33=cov['Q33'].value_counts()\n",
    "print(Q33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e96bbd52",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Question 34 Value Counts\n",
    "#\"Do you have any concerns about your position, department/unit, or services offered because of these changes?\n",
    "#Please skip this question if it is not applicable.\"\n",
    "\n",
    "#Open-Ended Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3f7e8d4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No     425\n",
      "Yes    154\n",
      "Name: Q35, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 35 Value Counts\n",
    "#Are you a solo librarian, school librarian, and/or librarian working in a small or rural library who may wear many hats, including handling some or all of the technical services tasks for your library?\n",
    "\n",
    "Q35=cov['Q35'].value_counts()\n",
    "print(Q35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5178c478",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oklahoma                            136\n",
      "Illinois                             86\n",
      "Texas                                26\n",
      "California                           25\n",
      "Ohio                                 24\n",
      "New York                             23\n",
      "Indiana                              19\n",
      "Pennsylvania                         18\n",
      "Massachusetts                        15\n",
      "Washington                           13\n",
      "Virginia                             12\n",
      "North Carolina                       11\n",
      "Maryland                              9\n",
      "Michigan                              8\n",
      "Georgia                               8\n",
      "New Jersey                            8\n",
      "Colorado                              8\n",
      "Minnesota                             8\n",
      "District of Columbia                  8\n",
      "Oregon                                7\n",
      "Iowa                                  7\n",
      "Florida                               6\n",
      "Connecticut                           6\n",
      "Arkansas                              5\n",
      "Tennessee                             4\n",
      "Kentucky                              4\n",
      "Louisiana                             4\n",
      "Wisconsin                             3\n",
      "Illinois                              3\n",
      "Alaska                                3\n",
      "Utah                                  3\n",
      "Alabama                               3\n",
      "South Carolina                        3\n",
      "Arizona                               3\n",
      "New Mexico                            3\n",
      "Kansas                                3\n",
      "Nevada                                3\n",
      "Delaware                              2\n",
      "South Dakota                          2\n",
      "New Hampshire                         2\n",
      "North Dakota                          2\n",
      "Indiana                               2\n",
      "Pennsylvania                          2\n",
      "Maine                                 1\n",
      "Hawaii                                1\n",
      "Government library in all states      1\n",
      "Kentucky                              1\n",
      "Southeast                             1\n",
      "Massachusetts                         1\n",
      "Iowa                                  1\n",
      "Idaho                                 1\n",
      "Nebraska                              1\n",
      "Florida                               1\n",
      "Hawaii                                1\n",
      "Missouri                              1\n",
      "Rhode Island                          1\n",
      "Name: Q36, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 36 Value Counts\n",
    "#What state is your library located in?\n",
    "\n",
    "Q36=cov['Q36'].value_counts()\n",
    "print(Q36)\n",
    "\n",
    "#Export to CSV for Visualization\n",
    "Q36.to_csv('Q36.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f8c0b487",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Academic Library                                             250\n",
      "Public Library                                               238\n",
      "Other                                                         45\n",
      "K-12 School Library                                           44\n",
      "Special Collections and/or Archive                            25\n",
      "Academic Library,Special Collections and/or Archive            8\n",
      "Academic Library,Other                                         2\n",
      "K-12 School Library,Other                                      1\n",
      "Academic Library,Special Collections and/or Archive,Other      1\n",
      "Name: Q37, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Question 37 Value Counts\n",
    "#What type of library do you work in?\n",
    "#Library Types\n",
    "Q37=cov['Q37'].value_counts()\n",
    "print(Q37)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1be45382",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Librarian            243\n",
       "Manager              203\n",
       " Supervisor          203\n",
       " Unit Head           203\n",
       "Library Assistant     88\n",
       " Associate            88\n",
       " Paraprofessional     88\n",
       "Dean or Director      54\n",
       "nan                   48\n",
       "Other                 26\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Question 38 Value Counts\n",
    "#What is your role at that library?\n",
    "\n",
    "#Convert column type to string\n",
    "cov['Q38'] = cov['Q38'].astype('str') \n",
    "\n",
    "#Tallied areas of technical services teams\n",
    "cov['Q38'].str.split(',', expand=True).stack().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fe0e1c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Visualizations\n",
    "#Grouped Bar Graphs For Before/Afters, Library Comparison Types\n",
    "#Pie Chart for Total Unit Comparisons\n",
    "#Stacked Bar Charts for Likert Questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "34404cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Areas Comprising Technical Services Units\n",
    "numbers=[616, 550, 459, 383, 301, 258, 202, 160, 76, 66]\n",
    "areas=['Cataloging', 'Processing', 'Acquisitions', 'Electronic Resources',\n",
    "       'Collection Development', 'Library Systems', 'Interlibrary Loan', \n",
    "       'Archives/Special Collections', 'Federal Depository', 'Other']\n",
    "\n",
    "df=pd.DataFrame({'Numbers':numbers, 'Areas': areas})\n",
    "\n",
    "df.plot.bar(x='Areas', y='Numbers', fontsize=8, rot=90, legend=False, \n",
    "            title='Areas Comprising Technical Services Units')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('TS_Units.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bcd4c71d",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Green Figures\n",
    "groups=['Pre-COVID','Post-COVID']\n",
    "numbers=[4040.1, 3674.3]\n",
    "\n",
    "groups_2=['Pre-COVID','Post-COVID']\n",
    "numbers_2=[6.4, 5.8]\n",
    "\n",
    "fig, axes=plt.subplots (nrows=1, ncols=2, figsize=(12,6))\n",
    "\n",
    "axes[0].bar(groups, numbers,color='darkgreen')\n",
    "axes[0].set_xlabel('Pre- and Post-COVID')\n",
    "axes[0].set_ylabel('Total Survey Sum')\n",
    "axes[0].set_title('Sum of All Positions')\n",
    "\n",
    "axes[1].bar(groups_2, numbers_2,color='darkgreen')\n",
    "axes[1].set_xlabel('Pre- and Post-COVID')\n",
    "axes[1].set_ylabel('Total Survey Average')\n",
    "axes[1].set_title('Average of All Positions')\n",
    "\n",
    "fig.suptitle('Technical Services Positions Pre- and Post-COVID')\n",
    "\n",
    "\n",
    "#Blue Figures\n",
    "groups=['Pre-COVID','Post-COVID']\n",
    "numbers=[4040.1, 3674.3]\n",
    "\n",
    "groups_2=['Pre-COVID','Post-COVID']\n",
    "numbers_2=[6.4, 5.8]\n",
    "\n",
    "fig, axes=plt.subplots (nrows=1, ncols=2, figsize=(12,6))\n",
    "\n",
    "axes[0].bar(groups, numbers)\n",
    "axes[0].set_xlabel('Pre- and Post-COVID')\n",
    "axes[0].set_ylabel('Total Survey Sum')\n",
    "axes[0].set_title('Sum of All Positions')\n",
    "\n",
    "axes[1].bar(groups_2, numbers_2)\n",
    "axes[1].set_xlabel('Pre- and Post-COVID')\n",
    "axes[1].set_ylabel('Total Survey Average')\n",
    "axes[1].set_title('Average of All Positions')\n",
    "\n",
    "fig.suptitle('Technical Services Positions Pre- and Post-COVID')\n",
    "\n",
    "plt.savefig('TS_Postions_Pre_Post.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a7f15603",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Positions Pre- and Post-COVID')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Green Figures\n",
    "groups=['Pre-COVID','Post-COVID']\n",
    "numbers=[4040.1, 3674.3]\n",
    "\n",
    "groups_2=['Pre-COVID','Post-COVID']\n",
    "numbers_2=[6.4, 5.8]\n",
    "\n",
    "fig=plt.figure()\n",
    "axes=fig.add_axes([0,0,1,1])\n",
    "axes.bar(groups, numbers,color='darkgreen')\n",
    "# axes.plot(color='darkgreen')\n",
    "axes.set_xlabel('Pre- and Post-COVID')\n",
    "axes.set_ylabel('Total Survey Sum')\n",
    "axes.set_title('Pre- and Post-COVID Sum of All Positions')\n",
    "\n",
    "fig=plt.figure()\n",
    "axes=fig.add_axes([0,0,1,1])\n",
    "axes.bar(groups_2, numbers_2,color='darkgreen')\n",
    "# axes.plot(color='darkgreen')\n",
    "axes.set_xlabel('Pre- and Post-COVID')\n",
    "axes.set_ylabel('Averages for All Respondents')\n",
    "axes.set_title('Average Positions Pre- and Post-COVID')\n",
    "\n",
    "#Blue Figures\n",
    "groups=['Pre-COVID','Post-COVID']\n",
    "numbers=[4040.1, 3674.3]\n",
    "\n",
    "groups_2=['Pre-COVID','Post-COVID']\n",
    "numbers_2=[6.4, 5.8]\n",
    "\n",
    "fig=plt.figure()\n",
    "axes=fig.add_axes([0,0,1,1])\n",
    "axes.bar(groups, numbers)\n",
    "axes.set_xlabel('Pre- and Post-COVID')\n",
    "axes.set_ylabel('Total Survey Sum')\n",
    "axes.set_title('Pre- and Post-COVID Sum of All Positions')\n",
    "\n",
    "fig=plt.figure()\n",
    "axes=fig.add_axes([0,0,1,1])\n",
    "axes.bar(groups_2, numbers_2)\n",
    "axes.set_xlabel('Pre- and Post-COVID')\n",
    "axes.set_ylabel('Averages for All Respondents')\n",
    "axes.set_title('Average Positions Pre- and Post-COVID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "09b8dfb5",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ELIZAB~1\\AppData\\Local\\Temp/ipykernel_16752/3963279116.py:20: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([' ', 'Academic', 'Public', 'K-12', 'Special Collections/Archives', 'Other', 'Blanks'],\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#By Total\n",
    "labels=['Academic', 'Public', 'K-12', 'Special Collections/Archives', 'Other', 'Blanks']\n",
    "before_covid=[1939.75, 1626.25, 86, 136, 225.6, 145.5]\n",
    "after_covid=[1685.75, 1516.2, 66, 131, 202.8, 127]\n",
    "\n",
    "x=np.arange(len(labels))\n",
    "width=0.35\n",
    "\n",
    "fig, ax=plt.subplots(figsize=(12, 8))\n",
    "rects1=ax.bar(x - width/2, before_covid, width, label='Pre-COVID')\n",
    "rects2=ax.bar(x + width/2, after_covid, width, label='Post-COVID')\n",
    "\n",
    "ax.set_ylabel('Library Type')\n",
    "ax.set_xlabel('Library Staffing Pre- and Post-COVID')\n",
    "ax.set_title('Library Staffing Numbers Pre- and Post-COVID by Library Type', pad=15)\n",
    "ax.legend()\n",
    "\n",
    "ax.bar_label(rects1, padding=3)\n",
    "ax.bar_label(rects2, padding=3)\n",
    "ax.set_xticklabels([' ', 'Academic', 'Public', 'K-12', 'Special Collections/Archives', 'Other', 'Blanks'], \n",
    "                   rotation=45, ha='right')\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('Staffing_Numbers_PrePost_ByType.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "58e3aecc",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ELIZAB~1\\AppData\\Local\\Temp/ipykernel_16752/1019283260.py:20: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([' ', 'Academic', 'Public', 'K-12', 'Special Collections/Archives', 'Other', 'Blanks'],\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#By Average\n",
    "labels=['Academic', 'Public', 'K-12', 'Special Collections/Archives', 'Other', 'Blanks']\n",
    "before_covid=[7.5, 7.0, 2.0, 4.1, 5.0, 4.0]\n",
    "after_covid=[6.6, 6.6, 1.5, 4.0, 4.4, 3.4]\n",
    "\n",
    "x=np.arange(len(labels))\n",
    "width=0.35\n",
    "\n",
    "fig, ax=plt.subplots(figsize=(12, 8))\n",
    "rects1=ax.bar(x - width/2, before_covid, width, label='Pre-COVID')\n",
    "rects2=ax.bar(x + width/2, after_covid, width, label='Post-COVID')\n",
    "\n",
    "ax.set_ylabel('Library Type')\n",
    "ax.set_xlabel('Library Staffing Pre- and Post-COVID')\n",
    "ax.set_title('Average Library Staffing Numbers Pre- and Post-COVID by Library Type', pad=15)\n",
    "ax.legend()\n",
    "\n",
    "ax.bar_label(rects1, padding=3)\n",
    "ax.bar_label(rects2, padding=3)\n",
    "ax.set_xticklabels([' ', 'Academic', 'Public', 'K-12', 'Special Collections/Archives', 'Other', 'Blanks'], \n",
    "                   rotation=45, ha='right')\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('Average_Staffing_Numbers_PrePost_ByType.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "084670b6",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "to               160\n",
       "of               134\n",
       "the              126\n",
       "and               83\n",
       "a                 80\n",
       "staff             56\n",
       "We                53\n",
       "in                53\n",
       "for               47\n",
       "had               43\n",
       "not               39\n",
       "we                38\n",
       "work              36\n",
       "have              35\n",
       "were              34\n",
       "that              32\n",
       "was               29\n",
       "with              27\n",
       "been              27\n",
       "as                25\n",
       "our               24\n",
       "lost              23\n",
       "on                21\n",
       "backlog           21\n",
       "time              20\n",
       "has               20\n",
       "due               20\n",
       "person            19\n",
       "who               18\n",
       "all               18\n",
       "one               18\n",
       "are               18\n",
       "position          18\n",
       "from              17\n",
       "but               17\n",
       "more              17\n",
       "loss              16\n",
       "library           16\n",
       "department        16\n",
       "at                16\n",
       "The               15\n",
       "knowledge         15\n",
       "is                15\n",
       "institutional     14\n",
       "people            13\n",
       "retirement        13\n",
       "cataloging        13\n",
       "out               12\n",
       "so                12\n",
       "or                11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most occurring words in Q12 column. \n",
    "cov.Q12.str.split(expand=True).stack().value_counts()[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "140c3330",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "to            392\n",
       "the           234\n",
       "and           155\n",
       "were          148\n",
       "for           145\n",
       "in            145\n",
       "of            139\n",
       "a             132\n",
       "We            102\n",
       "was            97\n",
       "work           90\n",
       "not            90\n",
       "we             89\n",
       "materials      82\n",
       "on             77\n",
       "services       67\n",
       "staff          66\n",
       "did            58\n",
       "library        58\n",
       "had            54\n",
       "physical       47\n",
       "time           46\n",
       "access         46\n",
       "from           46\n",
       "our            46\n",
       "impact         45\n",
       "that           44\n",
       "with           43\n",
       "no             42\n",
       "be             38\n",
       "building       36\n",
       "at             35\n",
       "No             34\n",
       "so             33\n",
       "technical      33\n",
       "more           33\n",
       "closed         32\n",
       "could          32\n",
       "only           31\n",
       "public         31\n",
       "new            28\n",
       "or             28\n",
       "The            28\n",
       "all            27\n",
       "cataloging     26\n",
       "processing     24\n",
       "up             24\n",
       "items          23\n",
       "during         23\n",
       "patrons        22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most occurring words in Q20 column. \n",
    "cov.Q20.str.split(expand=True).stack().value_counts()[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e20b0a8e",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "to           92\n",
       "the          70\n",
       "and          55\n",
       "of           49\n",
       "we           31\n",
       "is           29\n",
       "I            29\n",
       "that         28\n",
       "a            28\n",
       "work         26\n",
       "have         25\n",
       "our          25\n",
       "in           25\n",
       "will         22\n",
       "are          19\n",
       "for          18\n",
       "be           17\n",
       "not          17\n",
       "from         16\n",
       "more         16\n",
       "staff        16\n",
       "on           15\n",
       "We           15\n",
       "about        14\n",
       "as           14\n",
       "has          12\n",
       "materials    11\n",
       "time         11\n",
       "No           11\n",
       "with         11\n",
       "do           10\n",
       "my           10\n",
       "some         10\n",
       "no           10\n",
       "it           10\n",
       "people        9\n",
       "up            9\n",
       "were          9\n",
       "been          9\n",
       "by            9\n",
       "at            8\n",
       "now           8\n",
       "all           8\n",
       "or            8\n",
       "this          8\n",
       "was           8\n",
       "changes       8\n",
       "being         7\n",
       "what          7\n",
       "may           7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most occurring words in Q34 column. \n",
    "cov.Q34.str.split(expand=True).stack().value_counts()[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9f64e2fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data for Demographics Visualizations\n",
    "\n",
    "# Solo Librarian\n",
    "numbers=[154, 425]\n",
    "responses=['Yes', 'No']\n",
    "\n",
    "# State/Location\n",
    "numbers2=[261, 238, 45, 34, 47, 46]\n",
    "responses2=['Academic Library', 'Public Library', 'K-12 School Library', 'Special Collections/Archive',\n",
    "           'Other', 'No Response']\n",
    "\n",
    "# Role\n",
    "numbers3=[243, 203, 88, 54, 26, 48]\n",
    "responses3=['Librarian', 'Manager/Supervisor', 'Assistant/Paraprofessional', 'Dean/Director', 'Other', 'No Response']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2770aa53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# State/Location\n",
    "# Opted not to include State/Location visualization to avoid too busy of a visualization. \n",
    "\n",
    "# Utilized exported Q36 file to alphabetize results before copying/pasting into list\n",
    "numbers4=[3, 3, 3, 5, 25, 8, 6, 2, 8, 7, 8, 1, 2, 1, 89, 21, 8, 3, 5, 4, 1, 9, \n",
    "          16, 8, 8, 1, 1, 3, 2, 8, 3, 23, 11, 2, 24, 136, 7, 20, 1, 3, 2, 1, 4,\n",
    "          26, 3, 12, 13, 3]\n",
    "responses4=['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado',\n",
    "            'Connecticut', 'Delaware', 'District of Columbia', 'Florida', 'Georgia', \n",
    "            'Government library in all states', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', \n",
    "            'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts',\n",
    "            'Michigan', 'Minnesota', 'Missouri', 'Nebraska', 'Nevada', 'New Hampshire', \n",
    "            'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', \n",
    "            'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Rhode Island', 'South Carolina',\n",
    "            'South Dakota', 'Southeast', 'Tennessee', 'Texas', 'Utah', 'Virginia', 'Washington',\n",
    "            'Wisconsin']\n",
    "\n",
    "axes[1].bar(Q36)\n",
    "axes[1].set_xlabel('State Served')\n",
    "axes[1].set_ylabel('Number of Responses')\n",
    "axes[1].set_title('State or Location of Repondent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b30ac9e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Survey Respondent Demographics')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizations for Demographics\n",
    "fig, axes=plt.subplots (nrows=1, ncols=3, figsize=(12,6))\n",
    "\n",
    "# Solo Librarian\n",
    "axes[0].bar(responses, numbers)\n",
    "axes[0].set_xlabel(' ')\n",
    "axes[0].set_ylabel('Number of Responses')\n",
    "axes[0].set_title('Are You a Solo Librarian?')\n",
    "\n",
    "# Library Type\n",
    "axes[1].bar(responses2, numbers2)\n",
    "axes[1].set_xlabel(' ')\n",
    "axes[1].set_ylabel('Number of Responses')\n",
    "axes[1].set_title('Repository Type')\n",
    "\n",
    "# Role\n",
    "axes[2].bar(responses3, numbers3)\n",
    "axes[2].set_xlabel(' ')\n",
    "axes[2].set_ylabel('Number of Responses')\n",
    "axes[2].set_title('Repondent Role at Library')\n",
    "\n",
    "for ax in fig.axes:\n",
    "    ax.tick_params(labelrotation=90)\n",
    "    \n",
    "fig.suptitle('Survey Respondent Demographics')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
